Publications

Publications



2026

  • J. Tauberschmidt, S. Fellenz, S. J. Vollmer, and A. B. Duncan. Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems. Proceedings of the International Conference on Learning Representations (ICLR), (to appear) 2026. url: https://openreview.net/forum?id=khBHJz2wcV.
  • J. Abijuru, M. Nagda, P. Ostheimer, J. Tauberschmidt, S. Vollmer, S. Mandt, M. Kloft, and S. Fellenz. Heavy-tailed Physics-Informed Neural Networks. Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026.
  • J. Abijuru, M. Nagda, P. Ostheimer, S. Vollmer, M. Kloft, and S. Fellenz. Physics-Informed Residual Flows. Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026.
  • S. Ghansiyal, S. Hathwar, J. Platz, and J. C. Aurich. Physics-informed GANs for thermal simulation and anomaly detection in PBF-LB. Digital Engineering, 10:100110, 2026. doi: 10.1016/j.dte.2026.100110. url: https://doi.org/10.1016/j.dte.2026.100110.
  • S. Ghansiyal, L. Yi, P. M. Simon, M. Klar, and J. C. Aurich. Detecting inconspicuous anomalies in manufacturing using unsupervised anomaly detection. Procedia CIRP, 140:803-808, 2026. doi: 10.1016/j.procir.2026.05.135. url: https://doi.org/10.1016/j.procir.2026.05.135.

2025

  • S. Ghansiyal, M. Schmitz, T. Kirsch, M. Klar, and J. C. Aurich. Real-time process monitoring in additive manufacturing using machine learning. Procedia CIRP, 134:79-84, 2025. doi: 10.1016/j.procir.2025.03.046. url: https://doi.org/10.1016/j.procir.2025.03.046.
  • S. Ghansiyal, S. Ehmsen, M. Klar, and J. C. Aurich. Thermal simulations in additive manufacturing using machine learning. Procedia CIRP, 135:344-349, 2025. doi: 10.1016/j.procir.2024.12.029. url: https://doi.org/10.1016/j.procir.2024.12.029.